{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第0节：Matplotlib简介\n",
    "\n",
    "Matplotlib库由各种可视化类构成，内部结构复杂，受Matlab启发\n",
    "\n",
    "matplotlib.pyplot是绘制各类可视化图形的命令子库，相当于快捷方式\n",
    "\n",
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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oIVDqGn3XDbQJDeJHN8XU/QLlFkSE6aMTOXmhhPc2H7U7jlvRQqDUNVp9MI+dx87zzMgEAv20G/AkA+LC6d+hBa98fYjisgq747gNLQRKXYPvuoG2zRtzX59ou+Oo6zB9dCKni0p555sjdkdxG1oIlLoGX+07yb7jF5g8MgF/X/34eKLk2OYMTYzg1TWHKCwptzuOW9CfZKWcVFllmJGaToeIYO7u/b0psZQHmZaSyLnicv66IdvuKG5BC4FSTlq6+zjpp4qYOioRX73AjEfrGRNGSpdWvLYui4Ji7Qq0ECjlhIrKKmavyKBT6xDu0NlFG4RpKYkUllTw+vosu6PYTguBUk74bEcuWacvMjUlUS832UB0jmzKHd0jeXP9Yc5eLLM7jq20EChVh/LKKuasyqB7VCiju7SyO46qR1NTErhUXsmraw7ZHcVWWgiUqsPHaTkcO3uJaSmJetnJBia+ZQhjekXx9jfZ5BWW2B3HNloIlLqKkvJK5q7KIKltGMM66kWRGqLJIxMorzS8stp7uwItBEpdxQdbjnKioITpoztqN9BAxYYHc19SNO9tPsrx85fsjmMLLQRKXcGlskrmrT5Evw7NGRDXwu44ykLPjIzHYJi3OtPuKLaw8prFQSKyRUR2icg+EXmhljHDRKRARHY6br+2Ko9S1+pvm7I5XVSq3YAXiG7WmLE3teWjrcc4drbY7jguZ2VHUAqMMMb0BHoBtzouR1nTOmNML8ftRQvzKOW0otIKFqzJYnBCODfFNrc7jnKBicPj8fER5qzMsDuKy1l5zWJjjClyPPR33IxV61OqPr21ofrY8umjO9odRblI69Agxt3cjk+255CVX1T3CxoQS/cRiIiviOwE8oBUY8zmWob1d3x99IWIdL3C+4wXkTQRScvPz7cyslIUXCpn4dosRnVuSa+YMLvjKBd6algcgX6+zPayrsDSQmCMqTTG9AKigb4i0q3GkO1AO8fXR3OBxVd4n4XGmGRjTHJEhB7Cp6z1xrosLpRUMDUl0e4oysUiQgL5yYBYluw6zsGThXbHcRmXHDVkjDlP9cXrb63x/IXvvj4yxiwD/EUk3BWZlKrNuYtlvLkhm9u7t6Zrm1C74ygbPDmkA8EBfsxakW53FJex8qihCBEJc9xvBIwCvq0xprU4DscQkb6OPGesyqRUXV5dm8XFsgqmjNJuwFs1Cw7g8UHt+WLvSfbmFtgdxyWs7AgigdUishvYSvU+gqUiMkFEJjjG3AfsFZFdwBxgrDFGdygrW+QXlvL2xmzG9GxDYqsQu+MoG/10UHuaBvkxM9U7ugI/q97YGLMb6F3L8wsuuz8PmGdVBqWuxfyvD1FWWcVk7Qa8Xmgjf54cGsefvzrIjqPn6N22md2RLKVnFisFnCi4xKLNR7g3KYr24cF2x1Fu4NEBsTQPDmCGF3QFWgiUAl5enYkxhmdGJNgdRbmJ4EA/nhoax7qM02w5fNbuOJbSQqC83rGzxXy49RgPJMcQ07yx3XGUGxnXrx0RIYH8ZflBGvLuSy0EyuvNXZWBiDBpRLzdUZSbaRTgy8RhcWw5fJYNmQ33gEYtBMqrZZ++yCfbc3n45rZEhjayO45yQw/e3JY2oUG8lNpwuwItBMqrzV6ZQYCvD08Ni7M7inJTgX6+TBqRwI6j51l9MM/uOJbQQqC8VsapQhbvzOWRAe1oGRJkdxzlxu5PjqZt88bMSE1vkF2BU4VARBJFZKWI7HU87iEiv7Q2mlLWmrUig8b+vjw5RLsBdXX+vj48OzKBvbkX+GrfKbvj1DtnO4LXgOeBcvjXyWJjrQqllNX2HS/g8z0n+Omg9jQPDrA7jvIAd/dqQ4fwYGamplNV1bC6AmcLQWNjzJYaz1XUdxilXGVmagZNg/z46eAOdkdRHsLP14cpKYkcPFXI0j0n7I5Tr5wtBKdFJA7HhWVE5D6gYW0J5TV2HTvPigOnGD+kA6GN/O2OozzInd0j6dgqhFmp6VRUVtkdp944WwgmAq8CnUQkF5gCPGVZKqUs9FJqOs0a+/PowPZ2R1EexsdHmJqSQNbpiyzeedzuOPXGqUJgjMkyxowCIoBOxphBxphsS5MpZYGt2WdZm57PhKFxNAm0bM5F1YDd0rU1Xds0ZfbKdMobSFdw1U+CiEy7wvMAGGNmWJBJKcu8tPwg4U0CeaR/rN1RlIcSEaaPTuTxt9L4OC2Hh25ua3ekG1ZXRxDiuCVT/VVQlOM2AehibTSl6tfGzNNsyjrLxOFxNArwtTuO8mDDO7akd9sw5q3KoLSi0u44N+yqhcAY84Ix5gUgHEgyxkw3xkwH+lB9HWKlPIIxhpdS04kMDeLBvp7/F5yyl4gwPaUjxwtK+GDLMbvj3DBndxa3Bcoue1wGxF7tBSISJCJbRGSXiOwTkRdqGSMiMkdEMkVkt4gkOZ1cqWvwdXo+246cY9KIeIL8tRtQN25gfAv6tm/OvNWZXCrz7K7A2ULwN2CLiPxWRH4DbAbeqeM1pcAIY0xPoBdwq4j0qzHmNiDBcRsPzHc6uVJOMsYwY3k60c0acX+fGLvjqAaiuitIJL+wlEWbjtgd54Y4e9TQ74HHgXPAeeAxY8z/1PEaY4wpcjz0d9xqno43BnjHMXYTECYikdfyD3DW3twCfvzGZgoulVvx9sqNLd9/ij25BUwemUCAn06vperPzR1aMDghnPlrDlFU6rnn2Dr9qTDGbAPeBz4DzohInV+0ioiviOwE8qi+eP3mGkOigMu/YMtxPFfzfcaLSJqIpOXn5zsb+d+UVlTyzaEz/OzjXQ1y0ihVu6oqw8zUdDqEB/PD3t/70VLqhk1LSeTsxTLe3phtd5Tr5uykc3eJSAZwGFjj+O8Xdb3OGFNpjOlF9Y7lviLSreZb1/ayWt5noTEm2RiTHBER4Uzk7+nTrjnP396Z5ftPsXBt1nW9h/I8y/ae4NuThUwelYCfr3YDqv71btuMkZ1a8uqaQx77jYOzn4zfAf2AdGNMe2AUsMHZlRhjzgNfA7fWWJQDXP6lbTRg2el6jw+M5Y7ukfzpq4Nszmq4VxtS1Sod3UBiqybc2aON3XFUAzY1JZELJRW8sf6w3VGui7OFoNwYcwbwEREfY8xqqncAX5GIRIhImON+I6qLx7c1hi0BHnEcPdQPKDDGWDaHkYjwx3u70655Yya9v4O8CyVWrUq5gX/szOVQ/kWmjkrE16e25lOp+tEtKpTburXmzfWHOXexrO4XuBlnC8F5EWkCrAXeFZHZ1D37aCSwWkR2A1up3kewVEQmiMgEx5hlQBaQSfVU109f87/gGoUE+TN/XB+KSiqY9P6OBjVxlPo/5ZVVzF6ZQdc2Tbmla2u74ygvMDUlkYtlFbzqgV89O1sIxgDFwFTgS+AQ8IOrvcAYs9sY09sY08MY080Y86Lj+QXGmAWO+8YYM9EYE2eM6W6MSbv+f4rzOrYO4Q/3dGfL4bP8+auDrlilcrFPtuVw5Ewx01IS8dFuQLlAYqsQ7urZhrc3ZpNfWGp3nGtSZyEQEV/gH8aYKmNMhTHmbWPMHMdXRR7r7t5RjOvXllfXZvHVvpN2x1H1qLSikrmrMukVE8aITi3tjqO8yOSRCZRWVLJgzSG7o1yTOguBMaYSKBaRUBfkcalf3dmFntGh/OdHu8g+fdHuOKqefLj1GLnnLzF9dOK/JkhUyhU6RDThnqRo/rbpCCcLPGcfpLNfDZUAe0TkDceUEHNEZI6VwVwh0M+Xlx9OwtdXeOrd7ZSUe/Zp4gpKyiuZtyqTvrHNGRQfbncc5YUmj0ygqsrw8upMu6M4zdlC8DnwK6p3Fqc5btusCuVK0c0aM/NHvfj25AV+tXiv3XHUDVq06Qh5haXaDSjbxDRvzAM3xfDB1qPknCu2O45TrloIRGSMiEx07Bd4m+orlb0A/JbqnccNwvCOLXlmeDwfb8vhw61H7Y6jrtPF0grmf32IQfHh3Nyhhd1xlBd7ZkQ8IsLclZ7RFdTVEfyc6mP9vxNA9RTUw6i+JkGDMXlUIoMTwvnVP/axN7fA7jjqOrz9TTZnLpYxbXSi3VGUl4sMbcRDfdvy9+05HrH/sa5CEGCMuXwuoPXGmLPGmKNAsIW5XM7XR5j1o160CA7g6Xe3e+yp4t7qQkk5r67JYkSnliS1bWZ3HKV4engc/r7C7JUZdkepU12F4N8+UcaYSZc9vL5Jf9xYiyaBzHsoiePnLzH9o11UVenkdJ7izfWHKbhUzrQU7QaUe2gZEsRP+seyeGcuGacK7Y5zVXUVgs0i8kTNJ0XkSWCLNZHs1addM/7fHZ1ZceCUR54h6I3OF5fxxrrD3Nq1Nd2iGtxRzsqDPTk0jsb+vsxa4d5dwVUvXk/1mcSLReQhYLvjuT5AIHC3lcHs9OiAWNKOnOPPX31Lr5gw+sfpjkd3tnBtFkVlFUzVbkC5mebBATw2sD3zVmcy8fgFurRpanekWtV1zeI8Y8wAqmcfzXbcXjTG9DfGnLI+nj1EhP+9twftw4N55v3tnNLJ6dzW6aJS3tqYzZ092tCxdYjdcZT6nicGdyAkyI+ZK9LtjnJFzl6hbJUxZq7jtsrqUO6gSaAf88f14WJpJZPe2065Tk7nlhZ8fYiS8kqmjEqwO4pStQpt7M8TgzuQuv8Uu46dtztOrfRKHVeR2CqEP97bna3Z5/jTlzVn0FZ2O3WhhL9tOsIPe0cTF9HE7jhKXdFjA2Np1tifGanu2RVoIajDmF5RPNK/Ha+tO8yXey27VIK6Dq+szqSyyjB5pHYDyr2FBPnz5NA41qTnk5Z91u4436OFwAn/747O9IwJ42cf7+awB5wc4g1yz1/i/S3HuD85hrYtGtsdR6k6PdK/HeFNAnhpuft1BVoInBDo58srDyfh5ys8tWgbl8p0cjq7zVtVfTjeMyPibU6ilHMaB/jx9LB4vsk6w8bM03bH+TeWFQIRiRGR1SJyQET2icjkWsYME5ECEdnpuP3aqjw3KiqsEbPG9ubgqUJ+uXgvxujJZnY5cuYiH6fl8NDNbWkT1sjuOEo57aGb29K6aRAvpaa71e8QKzuCCmC6MaYz1Re+nygiXWoZt84Y08txe9HCPDdsaGIEz45I4JPtOXyw9VjdL1CWmL0yA18f4elhcXZHUeqaBPn7MmlEPNuOnGNNer7dcf7FskJgjDlhjNnuuF8IHACirFqfqzw7MoHBCeH8ZolOTmeHzLwiFu/I5ScDYmnZNMjuOEpdsweSY4hu1ogZbtQVuGQfgYjEAr2BzbUs7i8iu0TkCxHpeoXXjxeRNBFJy8+3t4r6+gizx/YmPDiACYu2UVCsk9O50qwV6QT5+/LkkA52R1HqugT4+fDsiAR25xSQut89zsu1vBCISBPgE2CKMeZCjcXbgXbGmJ7AXGBxbe9hjFlojEk2xiRHRNg/113z4ABefjiJUxdKmPbRTp2czkW+PXmBpbtP8NjAWFo0CbQ7jlLX7Z6kKGJbNGZGarpb/P6wtBCIiD/VReBdY8ynNZcbYy4YY4oc95cB/iLiEdcX7N22Gb+8owsrv81jvoddqNpTzUxNJyTIj/GDdd+A8mx+vj5MGZXItycLWeYG5ydZedSQAG8AB4wxM64wprVjHCLS15HnjFWZ6tsj/dvxg55teGn5QTa42eFgDc2enAK+2neK/xjUgdDG/nbHUeqG/aBnGxJaNmHWigwqbe4KrOwIBgI/BkZcdnjo7SIyQUS+u7rZfcBeEdkFzAHGGnfZe+IEEeGP93SnQ0QTnn1/BycLdHI6q8xIPUhYY38eHxRrdxSl6oWvjzA1JZHMvCKW7Mq1NYuVRw2tN8aIMabHZYeHLjPGLDDGLHCMmWeM6WqM6WmM6WeM2WhVHqsEB/qxYFwSl8p1cjqrbDtyjtUH83lySBwhQdoNqIbj1q6t6RzZlFkrMmz93aFnFteD+JYh/PHeHqQdOccfv9DJ6erbjNSDhDcJ4CcD2tkdRal65eMjTE9J5MiZYj7dnmNfDtvW3MDc1bMNjw6I5Y31h1m2x/6dPw3FN4fOsCHzDE8Ni6dxQF3XUVLK84zs3JKeMWHMWZlJaYU909doIahHv7i9M73bhvHzv+8mK7/I7jgezxjDjNSDtGoayNfqM/kAABFiSURBVMM3t7U7jlKWEKnuCnLPX+Ijm2Ys0EJQjwL8fHj5oSQC/Hx4atF2issq7I7k0dZlnGZr9jkmDY8nyN/X7jhKWWZwQjg3xTZj3upMSspd3xVoIahnbcIaMXtsL9LzCvnlZzo53fUyxvDS8oNEhTXigZti7I6jlKVEhGkpHTl1oZRFm464fP1aCCwwOCGCKSMT+XRHLu9tOWp3HI+08kAeu3IKeHZkPIF+2g2ohq9/XAsGxrdgwZpDXCx17bcJWggs8syIeIYmRvDCkv3sznHP65S6q6oqw4zUdGJbNOaepGi74yjlMtNSOnK6qIy3v8l26Xq1EFjEx0eY9aNeRIQE8tSi7ZwvLrM7ksf4ct9J9p+4wORRCfj76o+o8h592jVjWMcIFq7NorDEdRNa6qfMQs0ck9PlFZYw9UOdnM4ZlVWGmanpxLdswl09PX7WcqWu2fSUjpwvLufN9dkuW6cWAov1ignj13d2YfXBfF5enWl3HLf3z13HycgrYuqoRHx9xO44Srlc9+hQRndpxevrslz2TYIWAhcY168dY3q1YcaKdNZn6OR0V1JRWcXslRl0ah3Cbd1a2x1HKdtMG51IUVkFr63Lcsn6tBC4gIjwh3u6Ex/RhGc/2MGJgkt2R3JLn+7I5fDpi0wf3REf7QaUF+vUuil3dI/krxuyOVNUavn6tBC4SOMAP+aP60NpeSUT391OWYVOTne5sooqZq/IoGd0KKM6t7Q7jlK2mzIqkZLySha44HonWghcKL5lE/73vh5sP3qeP3xxwO44buWjtGPknr/E1JREHJeoUMqrxbdswt29o3jnmyPkXbB2instBC52Z482PDYwlr9uyGbp7uN2x3ELJeWVzFuVSXK7ZgxNtP9SpEq5i8kjE6ioMpYfaGLlFcpiRGS1iBwQkX0iMrmWMSIic0QkU0R2i0iSVXncyfO3dSapbRj/9ffdZObp5HTvbT7KyQslTBut3YBSl2vXIpgHkqN5f0t1x2wVKzuCCmC6MaYz0A+YKCJdaoy5DUhw3MYD8y3M4zYC/Hx4+eEkAv19efrdbV49OV1xWQWvfH2IAXEtGBDnEZerVsqlJo1IAGDeKuu6AiuvUHbCGLPdcb8QOADUPENoDPCOqbYJCBORSKsyuZPI0EbMGdubjLwifvHpHq+dnO6db45wuqiU6aMT7Y6ilFuKCmvE2L4xfJx2jKNnii1Zh0v2EYhILNAb2FxjURRw+QTcOXy/WCAi40UkTUTS8vPzrYrpcoMSwpk2KpHFO4+zaLP3TU5XVFrBq2sOMTQxgj7tmtsdRym3NXF4PL4+wqtrrTmCyPJCICJNgE+AKcaYCzUX1/KS7/1pbIxZaIxJNsYkR0Q0rJ2JE4fHM7xjBL/75352HfOuyen+uv4w54rLtRtQqg6tmgbx+k+See62Tpa8v6WFQET8qS4C7xpjPq1lSA5w+WTz0YBXHUrj4yPMdExO9/S72zl30TsmpysoLmfhuixSurSiR3SY3XGUcnuDEyIICfK35L2tPGpIgDeAA8aYGVcYtgR4xHH0UD+gwBjjdRf8DWscwPxxSeQXljLFSyane319FoUlFUxL0W5AKbtZ2REMBH4MjBCRnY7b7SIyQUQmOMYsA7KATOA14GkL87i1HtFh/PoHXViTns9cC48OcAdnL5bx5vrD3NEjks6RTe2Oo5TX87PqjY0x66l9H8DlYwww0aoMnubhm9uy7cg5Zq1Mp3fbMIY00JOrXl1ziEvllUwdlWB3FKUUemaxWxERfv/DbiS0bMLkD3Zw3MITSOySV1jC299kc3evKOJbhtgdRymFFgK3893kdOWVhqcb4OR0r6w+RHml4dmR2g0o5S60ELihuIgm/Om+Huw8dp7/WdZwJqc7fv4S720+yn1J0cSGB9sdRynloIXATd3ePZKfDmrPWxuzWbKrYRxRO291JgbDMyPj7Y6ilLqMFgI39txtnUhu14znPtlNZl6h3XFuyLGzxXy09Rhjb2pLdLPGdsdRSl1GC4Eb8/f1Yd5DSTQO8GXCou1cLPXcyenmrMzA10eYNEK7AaXcjRYCN9c6NIg5Y3uTlV/E8x46OV1WfhGfbM9hXL92tGoaZHccpVQNWgg8wID4cKaP7siSXcf526Yjdse5ZrNXZhDo58tTw+LsjqKUqoUWAg/x1NA4RnZqye+W7mfH0XN2x3Fa+qlCluw6zqMDYwlvEmh3HKVULbQQeAgfH2HGA71o1TSIie9u56yHTE43MzWd4AA/xg/uYHcUpdQVaCHwIKGN/Zn/cB9OF5Ux+YMdVLr55HR7cwv4Yu9JfjqoPc2CA+yOo5S6Ai0EHqZ7dCi/vasr6zJOM2dlht1xrmpmajqhjfz56eD2dkdRSl2FFgIP9GDfGO5JimLOqgy+Pphnd5xa7Th6jpXf5jF+SAeaWjSHulKqfmgh8EAiwu/v7k7HViFM+XAnuW44Od2M1HSaBwfw6IBYu6MopeqghcBDNQrw5ZWHk6hwTE5XWlFpd6R/2XL4LOsyTvPU0DiCAy2b6VwpVU+0EHiwDhFN+Mv9Pdh17Dy//9w9JqczxvCX5QdpGRLIuH7t7I6jlHKClZeqfFNE8kRk7xWWDxORgsuuXvZrq7I0ZLd2i+SJwe1555sj/GNnrt1x2JB5hi2HzzJxeDyNAnztjqOUcoKVHcFbwK11jFlnjOnluL1oYZYG7ee3duKm2GY898keMk7ZNzmdMYaXUg/SJjSIsX1jbMuhlLo2lhUCY8xa4KxV76/+z3eT0wUH+jFh0TaKbJqc7uuD+ew4ep5nRiYQ6KfdgFKewu59BP1FZJeIfCEiXa80SETGi0iaiKTl5+e7Mp/HaNU0iLkP9ubw6Ys898lul09O91030LZ5Y+7rE+3SdSulboydhWA70M4Y0xOYCyy+0kBjzEJjTLIxJjkiomFe0L0+9I9rwX/e0pGlu0/w9sZsl677q32n2Jt7gckjE/D3tfvvC6XUtbDtE2uMuWCMKXLcXwb4i0i4XXkaiglD4hjVuSX//fkBth1xzeR0VVWGmanpdIgI5u7eUS5Zp1Kq/thWCESktYiI435fR5YzduVpKHx8hJfu70VkWBCT3tvOmaJSy9e5dM8JDp4qZMqoRHx9xPL1KaXql5WHj74PfAN0FJEcEfmpiEwQkQmOIfcBe0VkFzAHGGs88aorbui7yenOXCxj8gc7LZ2crqKyilkr0unYKoQ7u0dath6llHUsO+3TGPNgHcvnAfOsWr+36xYVyot3deW5T/cwe0U600Z3tGQ9i3ceJyv/IgvG9cFHuwGlPJLu1WvAfnRTDPf1iWbOqkxWWzA5XXllFXNWZtAtqim3dG1V7++vlHINLQQNmIjwuzHd6NQ6hKkf7iTnXHG9vv/ft+Vw9Gwx01M64tjdo5TyQFoIGrhGAb4sGNeHynqenK60opK5KzPo3TaMYR31kF6lPJkWAi8QGx7Mn+/vye6cAn63dH+9vOcHW45xvKCE/xyt3YBSnk4LgZe4tVtrnhzSgUWbjrJ4x41NTneprJJ5qzO5uX1zBsS1qKeESim7aCHwIj+7pSN92zfn+U/3kH4Dk9Mt2nSE/MJSpms3oFSDoIXAi/j5+jDvwd43NDndxdIK5q85xOCEcPq2b25BSqWUq2kh8DItmwYx76HeHDlTzH/9/donp3trYzZnL5Yx3aLzEpRSrqeFwAv169CCn93Skc/3nODNDdlOv67gUjmvrjnEqM4t6RUTZl1ApZRLaSHwUk8O6UBKl1b8YdkB0rKdu2zEG+sPc6GkgqkpiRanU0q5khYCLyUi/OX+nkQ1a8TE97Zzuo7J6c5dLOPN9Ye5rVtrurYJdVFKpZQraCHwYqGN/Hnl4STOF5cz+YMdV52cbuG6LC6WaTegVEOkhcDLdW0Tyu/GdGND5hlmpqbXOia/sJS3NmRzV882JLYKcXFCpZTVtBAoHrgphgeSo5m3OpNV35763vIFaw5RVlnF5JEJNqRTSllNC4EC4MUx3egS2ZSpH+7i2Nn/m5zuZEEJf9t0hHt6R9EhoomNCZVSVtFCoAAI8vdl/rgkqkz15HQl5dWT0728OpOqKsOz2g0o1WBZeYWyN0UkT0T2XmG5iMgcEckUkd0ikmRVFuWcdi2Ceen+nuzJLeDFpfvJOVfMB1uP8qObYohp3tjueEopi1jZEbwF3HqV5bcBCY7beGC+hVmUk0Z3bc2EoXG8t/koj7+1FRFh0oh4u2MppSxkWSEwxqwFrnam0hjgHVNtExAmInrRWzfwn6MT6dehOemninj45rZEhjayO5JSykJ27iOIAo5d9jjH8dz3iMh4EUkTkbT8/HyXhPNmfr4+zH0wiQlD43h2hO4bUKqhs7MQ1DZ/ca1nNBljFhpjko0xyRERejUsV4gICeS52zrRLDjA7ihKKYvZWQhygJjLHkcDx23KopRSXsvOQrAEeMRx9FA/oMAYc8LGPEop5ZX8rHpjEXkfGAaEi0gO8BvAH8AYswBYBtwOZALFwGNWZVFKKXVllhUCY8yDdSw3wESr1q+UUso5emaxUkp5OS0ESinl5bQQKKWUl9NCoJRSXk6q99l6DhHJB45c58vDgdP1GKe+uGsucN9smuvaaK5r0xBztTPG1HpGrscVghshImnGmGS7c9TkrrnAfbNprmujua6Nt+XSr4aUUsrLaSFQSikv522FYKHdAa7AXXOB+2bTXNdGc10br8rlVfsIlFJKfZ+3dQRKKaVq0EKglFJerkEWAhG5VUQOikimiDxXy3IRkTmO5btFJMlNcg0TkQIR2em4/dpFud4UkTwR2XuF5XZtr7pyuXx7iUiMiKwWkQMisk9EJtcyxuXby8lcdmyvIBHZIiK7HLleqGWMHdvLmVy2fB4d6/YVkR0isrSWZfW/vYwxDeoG+AKHgA5AALAL6FJjzO3AF1RfJa0fsNlNcg0DltqwzYYAScDeKyx3+fZyMpfLtxcQCSQ57ocA6W7y8+VMLju2lwBNHPf9gc1APzfYXs7ksuXz6Fj3NOC92tZvxfZqiB1BXyDTGJNljCkDPgDG1BgzBnjHVNsEhIlIpBvksoUxZi1w9ipD7NhezuRyOWPMCWPMdsf9QuAA37/Wtsu3l5O5XM6xDYocD/0dt5pHqNixvZzJZQsRiQbuAF6/wpB6314NsRBEAccue5zD9z8QzoyxIxdAf0e7+oWIdLU4k7Ps2F7Osm17iUgs0JvqvyYvZ+v2ukousGF7Ob7m2AnkAanGGLfYXk7kAnt+vmYBPweqrrC83rdXQywEUstzNSu9M2PqmzPr3E71fCA9gbnAYoszOcuO7eUM27aXiDQBPgGmGGMu1Fxcy0tcsr3qyGXL9jLGVBpjelF9XfK+ItKtxhBbtpcTuVy+vUTkTiDPGLPtasNqee6GtldDLAQ5QMxlj6OB49cxxuW5jDEXvmtXjTHLAH8RCbc4lzPs2F51smt7iYg/1b9s3zXGfFrLEFu2V1257P75MsacB74Gbq2xyNafryvlsml7DQTuEpFsqr8+HiEii2qMqfft1RALwVYgQUTai0gAMBZYUmPMEuARx973fkCBMeaE3blEpLWIiON+X6r//5yxOJcz7NhedbJjeznW9wZwwBgz4wrDXL69nMll0/aKEJEwx/1GwCjg2xrD7NhedeayY3sZY543xkQbY2Kp/h2xyhgzrsawet9ell2z2C7GmAoRmQR8RfWROm8aY/aJyATH8gXAMqr3vGcCxcBjbpLrPuApEakALgFjjeMwASuJyPtUHyERLiI5wG+o3nlm2/ZyMpcd22sg8GNgj+P7ZYBfAG0vy2XH9nImlx3bKxJ4W0R8qf5F+pExZqndn0cnc9nyeayN1dtLp5hQSikv1xC/GlJKKXUNtBAopZSX00KglFJeTguBUkp5OS0ESinl5bQQKKWUl9NCoJRSXu7/Awo3XohLseF+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# plt.plot()只有一个输入列表或数组时，参数被当作Y轴，X轴以索引自动生成\n",
    "plt.plot([3,1,4,5,2])\n",
    "\n",
    "plt.ylabel('Grade')\n",
    "\n",
    "# plt.savefig()将输出图形存储为文件，默认PNG格式，可以通过dpi修改输出质量\n",
    "plt.savefig('example.pdf',dpi = 1000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.plot(x,y)当有两个以上参数时，按照X轴和Y轴顺序绘制数据点\n",
    "x = [0,2,4,6,8]\n",
    "y = [3,1,4,5,2]\n",
    "plt.ylabel(\"Grade\")\n",
    "\n",
    "# plt.axis(x1,x2,y1,y2)\n",
    "# x轴范围[x1,x2]\n",
    "# y轴范围[y1,y2]\n",
    "plt.axis([-1,10,0,6])\n",
    "\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot绘图区域\n",
    "# 同时绘制多张图\n",
    "\n",
    "# plt.subplot(nrows, ncols, plot_number)\n",
    "# 把整个画图区域分为 nrows 行，ncols列，在 plot_number 上绘制图表\n",
    "# plt.subplot(2,3,4)表示的如图所示"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![avatar](./image/day3-img1.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def f(t):\n",
    "    return np.exp(-t) * np.cos(2*np.pi*t)\n",
    "\n",
    "def g(t):\n",
    "    return np.cos(2*np.pi*t)\n",
    "\n",
    "a = np.arange(0,5,0.02)\n",
    "\n",
    "plt.subplot(2,1,1) # 等价于plt.subplot(211)\n",
    "plt.plot(a,f(a))\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(a,g(a),'r--')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# 第1节：plot函数\n",
    "plt.plot(x, y, format_string, $**kwargs$)\n",
    "\n",
    "+ x : X轴数据，列表或数组，可选\n",
    "+ y : Y轴数据，列表或数组\n",
    "+ format_string: 控制曲线的格式字符串，可省略\n",
    "+ $**kwargs$ : 第二组或更多(x,y,format_string)\n",
    "\n",
    "+ **当绘制多条曲线时，各条曲线的x不能省略**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 正态分布\n",
    "def f(x, mu, sig):\n",
    "    return 1/(np.sqrt(2*np.pi)*sig)*np.exp(-1*(x-mu)**2/2*sig**2)\n",
    "\n",
    "x = np.arange(-5, 5, 0.01)\n",
    "\n",
    "plt.plot(x, f(x, 0, 1), x, f(x, 0, 0.5))\n",
    "# 曲线1：x = x，y = f(x,0,1)\n",
    "# 曲线2：x = x, y = f(x,0,0.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### format_string = 颜色字符 + 风格字符 + 标记字符\n",
    "\n",
    "---\n",
    "\n",
    "# 颜色字符\n",
    "\n",
    "![avatar](./image/day3-img2.PNG)\n",
    "\n",
    "---\n",
    "\n",
    "# 风格字符\n",
    "\n",
    "![avatar](./image/day3-img3.PNG)\n",
    "\n",
    "---\n",
    "\n",
    "# 标记字符\n",
    "\n",
    "![avatar](./image/day3-img4.PNG)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 正态分布\n",
    "def f(x, mu, sig):\n",
    "    return 1/(np.sqrt(2*np.pi)*sig)*np.exp(-1*(x-mu)**2/2*sig**2)\n",
    "\n",
    "\n",
    "x = np.arange(-5, 5, 0.1)\n",
    "\n",
    "#plt.plot(x, f(x, 0, 1), x, f(x, 0, 0.5),':')\n",
    "plt.plot(x, f(x, 0, 1),'r-.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 一些其他的参数\n",
    "\n",
    "# color : 控制颜色, color='green'\n",
    "# linestyle : 线条风格, linestyle='dashed'\n",
    "# marker : 标记风格, marker='o'\n",
    "# markerfacecolor: 标记颜色, markerfacecolor='blue'\n",
    "# markersize : 标记尺寸, markersize=20\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 正态分布\n",
    "def f(x, mu, sig):\n",
    "    return 1/(np.sqrt(2*np.pi)*sig)*np.exp(-1*(x-mu)**2/2*sig**2)\n",
    "\n",
    "x = np.arange(-5, 5, 0.3)\n",
    "\n",
    "#plt.plot(x, f(x, 0, 1), x, f(x, 0, 0.5),':')\n",
    "plt.plot(x, f(x, 0, 1), color='pink', marker='x')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# 第2节：plot的中文显示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "\n",
    "# 最简单的方法\n",
    "plt.rcParams['font.family']=['SimHei'] #显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 正态分布\n",
    "def f(x, mu, sig):\n",
    "    return 1/(np.sqrt(2*np.pi)*sig)*np.exp(-1*(x-mu)**2/2*sig**2)\n",
    "\n",
    "x = np.arange(-5, 5, 0.3)\n",
    "\n",
    "plt.xlabel('x轴')\n",
    "plt.ylabel('y轴')\n",
    "\n",
    "plt.plot(x, f(x, 0, 1), 'c--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### rcParams的属性\n",
    "+ 'font.family' 用于显示字体的名字\n",
    "+ 'font.style' 字体风格，正常'normal'或 斜体'italic'\n",
    "+ 'font.size' 字体大小，整数字号或者'large'、'x‐small'\n",
    "\n",
    "---\n",
    "\n",
    "**字体**\n",
    "\n",
    "+ 'SimHei' 中文黑体\n",
    "+ 'Kaiti' 中文楷体\n",
    "+ 'LiSu' 中文隶书\n",
    "+ 'FangSong' 中文仿宋\n",
    "+ 'YouYuan' 中文幼圆\n",
    "+ 'STSong' 华文宋体"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 方法2：在有中文输出的地方，增加一个属性：fontproperties\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "matplotlib.rcParams['font.family'] = 'SimHei'\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "# 正态分布\n",
    "\n",
    "\n",
    "def f(x, mu, sig):\n",
    "    return 1/(np.sqrt(2*np.pi)*sig)*np.exp(-1*(x-mu)**2/2*sig**2)\n",
    "\n",
    "\n",
    "x = np.arange(-5, 5, 0.3)\n",
    "\n",
    "plt.xlabel('x轴',fontproperties='SimHei',fontsize = 15)\n",
    "plt.ylabel('y轴',fontproperties='SimHei',fontsize = 15)\n",
    "\n",
    "plt.plot(x, f(x, 0, 1), 'c--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# 第3节：plot文本显示\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.xlabel() 对X轴增加文本标签\n",
    "# plt.ylabel() 对Y轴增加文本标签\n",
    "\n",
    "# plt.title() 对图形整体增加文本标签\n",
    "\n",
    "# plt.text(x,y,words) 在(x,y)处增加文本words\n",
    "\n",
    "# plt.annotate() 在图形中增加带箭头的注解\n",
    "# plt.annotate(s, xy=arrow_crd, xytext=text_crd, arrowprops=dict)\n",
    "# xy：箭头位置\n",
    "# xytext：文本位置\n",
    "# arrowprops：样式\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "matplotlib.rcParams['font.family'] = 'SimHei'\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 正态分布\n",
    "def f(x, mu, sig):\n",
    "    return 1/(np.sqrt(2*np.pi)*sig)*np.exp(-1*(x-mu)**2/2*sig**2)\n",
    "\n",
    "\n",
    "x = np.arange(-5, 5, 0.3)\n",
    "\n",
    "plt.title('正态分布曲线')\n",
    "plt.xlabel('x轴')\n",
    "plt.ylabel('y轴')\n",
    "\n",
    "plt.text(2, 0.3, 'u = 0,sigma = 1')\n",
    "plt.annotate('标准正态分布', xy=(1, 0.3), xytext=(1.5, 0.4),\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1, width=1))\n",
    "\n",
    "plt.grid(True)  # 是否设置网格线\n",
    "plt.plot(x, f(x, 0, 1), 'r--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# 第4节：子绘图区域（了解）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# subplot2grid(shape, loc, rowspan=1, colspan=1, fig=None, **kwargs)\n",
    "\n",
    "# shape：放置子图的网格规格，包含两个整型的元组，第一个代表网格的行数，第二个代表网格的列数\n",
    "# loc：子图所在的位置，包含两个整型的元组，第一个代表子图所在的行数，第二个代表子图所在的列数\n",
    "\n",
    "ax1 = plt.subplot2grid((3,3), (0,0), colspan=3)  \n",
    "ax2 = plt.subplot2grid((3,3), (1,0), colspan=2)  #col 显示图形占2列\n",
    "ax3 = plt.subplot2grid((3,3), (1, 2), rowspan=2)  #row 显示图形占2行\n",
    "ax4 = plt.subplot2grid((3,3), (2, 0))  \n",
    "ax5 = plt.subplot2grid((3,3), (2, 1))  \n",
    "plt.suptitle(\"subplot2grid\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# 一个新的类（了解）\n",
    "![avatar](./image/day3-img5.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.1 64-bit",
   "language": "python",
   "name": "python38164bitfe2ebe8b9a4f493e93d81a74c550882b"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
